Parallel fiber coding in the cerebellum for life-long learning

Continuous and real-time learning is a difficult problem in robotics. To le arn efficiently, it is important to recognize the current situation and lea rn appropriately for that context. To be effective, this requires the integ ration of a large number of sensorimotor and cognitive signals. So far, few principles on how to perform this integration have been proposed. Another limitation is the difficulty to include the complete contextual information to avoid destructive interference while learning different tasks. We suggest that a vertebrate brain structure important for sensorimotor coo rdination, the cerebellum, may provide answers to these difficult problems. We investigate how learning in the input layer of the cerebellum may succe ssfully encode contextual knowledge in a representation useful for coordina tion and life-long learning. We propose that a sparsely-distributed and sta tistically-independent representation provides a valid criterion for the se lf-organizing classification and integration of context signals. A biologic ally motivated unsupervised learning algorithm that approximate such a repr esentation is derived from maximum likelihood. This representation is benef icial for learning in the cerebellum by simplifying the credit assignment p roblem between what must be learned and the relevant signals in the current context for learning it. Due to its statistical independence, this represe ntation is also beneficial for life-long learning} by reducing the destruct ive interference across tasks, while retaining the ability to generalize. T he benefits of the learning algorithm are investigated in a spiking model t hat learns to generate predictive smooth pursuit eye movements to follow ta rget trajectories.